Communication channel equalization based on Levenberg-Marquardt trained artificial neural networks

Transmitting digital signals through frequency selective communication channel, several problems arise, such as additive noise and ISI (Inter-Symbol Interference). To compensate distortions caused by these factors and to find the original information being transmitted, equalization process is performed at the receiver. Previous authors have shown that nonlinear feed-forward equalizers based on either MLP (Multi Layer Perceptron) or RBF (Radial Basis Function) can outperform linear equalizers. In this paper, we suggest an adaptive neural network equalizer using Levenberg-Marquardt training algorithm, (MLP-LM), which considerably reduces the learning MSE (Mean Square Error) and eliminates efficiently the effects of ISI comparatively to the MLP-BP, RBF and conventional equalizers.